Relating Sediment Yield Estimations to the Wet Front Term Using Rainfall Simulator Field Experiments

Water Resources Management - Depth to wet front is generally considered as the amount of water that penetrates into soil and wets the internal soil layer. This is an important variable especially...     

Knowledge Diffusion of the Internet of Things (IoT): A Main Path Analysis

Wireless Personal Communications - The Internet of Things (IoT) is a concept that has attracted significant attention since the emergence of wireless technology. The knowledge diffusion of IoT...     

Modeling industrial thickener using computational fluid dynamics (CFD), a case study: Tailing thickener in the Sarcheshmeh copper mine

Separation of particles from liquid in the large gravitational tanks is widely used in mining and industrial wastewater treatment process. Thickener is key unit in the operational processes of hydrometallurgy and is used to separate solid from liquid. In this study, population balance models were combined with computational fluid dynamics (CFD) for modeling the tailing thickener. Parameters such as feed flow rate, flocculant dosage, inlet solid percent and feedwell were investigated. CFD was used to simulate the industrial tailing thickener with settled bed of 120 m diameter which is located in the Sarcheshmeh copper mine. Important factor of drag force that defines the rake torque of rotating paddles on the bed was also determined. Two phases turbulence model of Eulerian/Eulerian in accordance with turbulence model of k-ε was used in the steady-state. Also population balance model consists of 15 groups of particle sizes with Luo and Lehr kernel was used for aggregation/breakage kernel. The simulation results showed good agreement with the operational data.     

Effect of galvanic contact on the flotability of galena in the presence and absence of a collector

In this paper, the effect of various electrochemical environments in the galena flotation is investigated. The electrochemical environments consisted of a ball mill for grinding of galena ore and a Denver flotation cell for flotation of galena in the laboratory scale. In order to achieve the maximum recovery with sodium hyposulfite, the concentrations of 0.06 and 0.12 M of air and nitrogen gases have been used to control the pulp potential in the Denver flotation cell. The galena sample was from the “Era mine” which is located in the Kiyasar area, north of Iran. This mine contains: Galena (PbS) at least 22%, Fluorite (CaF₂) 73.37%, Quartz (SiO₂) 2.54% and other minerals such as Cerussite (PbCO₃) and Kaolinite (Al₂Si₂O₅(OH)₄). Flotation of Galena was conducted in a 0.12 M of sodium hyposulfite solution. It was found that the amount of recovery by this process was 85% and 63% in the presence and absence of potassium amyl xanthate (KAX) and at an estimated potential of 280 to 350 mV respectively while the recovery was 70% and 52% at an approximate potential of 175 to 210 mV in water in the presence and absence of collector (KAX).     

Stable identification of nonlinear systems using neural networks: theory and experiments

This paper presents an approach for stable identification of multivariable nonlinear system dynamics using a multilayer feedforward neural network. Unlike most of the previous neural network identifiers, the proposed identifier is based on a nonlinear-in-parameters neural network (NLPNN). Therefore, it is applicable to systems with higher degrees of nonlinearities. Both parallel and series-parallel models are used with no a priori knowledge about the system dynamics. The method can be considered both as an online identifier that can be used as a basis for designing a neural network controller as well as an offline learning scheme for monitoring the system states. A novel approach is proposed for the weight updating mechanism based on the modification of the backpropagation (BP) algorithm. The stability of the overall system is shown using Lyapunov's direct method. To demonstrate the performance of the proposed algorithm, an experimental setup consisting of a three-link macro-micro manipulator (M/sup 3/) is considered. The proposed approach is applied to identify the dynamics of the experimental robot. Experimental and simulation results are given to show the effectiveness of the proposed learning scheme.     

A high-performance and scalable multi-core aware software solution for network monitoring

In recent years, the need for high-performance network monitoring tools, which can cope with rapidly increasing network bandwidth, has become vital. A possible solution is to utilize the processing power of multi-core processors that nowadays are available as commercial-off-the-shelf (COTS) hardware. In this paper, we introduce a software solution for wire-speed packet capturing and transmission for TCP/IP networks under Linux operating system, called DashCap. The results of our experimental evaluations show that the proposed solution causes more than two times performance boost for packet capturing in comparison to the existing software solutions under Linux. We have proposed a scalable software architecture for network monitoring tools called DashNMon, which is based on DashCap. Multi-core awareness is a distinguished property of this architecture. Comparing to the existing cluster-based solutions, DashNMon can be used with COTS multi-core processors. In order to evaluate the proposed solutions, we have developed several prototype tools. The results of the experiments carried out using these tools show the scalability and high performance of the network monitoring tools that are based on the proposed architecture. Using the proposed architecture, it is possible to design and implement high-performance multi-threaded network intrusion detection systems (NIDSs) or application-layer firewalls, completely in the user space and with better utilization of the computational resources of multi-processor/multi-core systems.     

Effects of Desolvating Agent Types,Ratios, and Temperature on Size and Nanostructure of Nanoparticles from α‐Lactalbumin and Ovalbumin

In this study, we compare the preparation of ovalbumin (OVA) and α‐lactalbumin (α‐LA) nanoparticles using different desolvating agents (ethanol, acetone, and methanol) and water: desolvating agent volume ratios (1:3, 1:4, 1:5, 1:10, and 1:20). Also the effects of protein solution temperature (25, 50, and 80 ℃) on the size of nanoparticles and the stability of crosslinked nanoparticles for 30 d were studied. OVA and α‐LA were shown to be good candidates for nanoparticulation and nanoparticles in the range of 60 to 230 nm were obtained. The comparison between the 2 proteins offers guidance to optimize OVA and α‐LA nanoparticle fabrication and to efficiently obtain nanoparticles with desired characteristics. The particle sizes of OVA nanoparticles were found to be in the range of 60 to 160 nm, and the particle sizes of α‐LA were between 150 and 230 nm. The sizes varied with different desolvating agents: for OVA, ethanol, and methanol both produced nanoparticles smaller than 100 nm; for α‐LA, methanol produced the smallest nanoparticles. Water: desolvating agent ratios, in the studied range, did not show a significant effect on the particle sizes for both OVA and α‐LA nanoparticles. The size and morphology of the nanoparticles were found to change when the protein solutions were heated up to 50 and 80 ℃ and cooled down before nanoparticulation and most nanoparticles had a smaller diameter.     

Nurr1 Is Not an Essential Regulator of BDNF in Mouse Cortical Neurons

Nurr1 and brain-derived neurotrophic factor (BDNF) play major roles in cognition. Nurr1 regulates BDNF in midbrain dopaminergic neurons and cerebellar granule cells. Nurr1 and BDNF are also highly expressed in the cerebral cortex, a brain area important in cognition. Due to Nurr1 and BDNF tissue specificity, the regulatory effect of Nurr1 on BDNF in different brain areas cannot be generalized. The relationship between Nurr1 and BDNF in the cortex has not been investigated previously. Therefore, we examined Nurr1-mediated BDNF regulation in cortical neurons in activity-dependent and activity-independent states. Mouse primary cortical neurons were treated with the Nurr1 agonist, amodiaquine (AQ). Membrane depolarization was induced by KCl or veratridine and reversed by nimodipine. AQ and membrane depolarization significantly increased Nurr1 (p < 0.001) and BDNF (p_AQ < 0.001, p_KCl < 0.01) as assessed by real-time qRT-PCR. However, Nurr1 knockdown did not affect BDNF gene expression in resting or depolarized neurons. Accordingly, the positive correlation between Nurr1 and BDNF expression in AQ and membrane depolarization experiments does not imply co-regulation because Nurr1 knockdown did not affect BDNF gene expression in resting or depolarized cortical neurons. Therefore, in contrast to midbrain dopaminergic neurons and cerebellar granule cells, Nurr1 does not regulate BDNF in cortical neurons.     

Using 1H‐NMR spectroscopy for the kinetic study of the in situ solution free‐radical copolymerization of styrene and ethyl acrylate

The free‐radical copolymerization of styrene and ethyl acrylate in benzene‐d₆ as the solvent in the presence of benzoyl peroxide as an initiator at 70°C was studied by online ¹H‐NMR spectroscopy. The chemical composition of the copolymer at different reaction times was calculated from the conversion of the monomers to the copolymer, and then the reactivity ratios of styrene and ethyl acrylate were determined at both low and high conversions. Data for the overall monomer conversion versus the time were used to estimate the ratio k_pk_t^?0.5 for different compositions of the initial feed (k_p is the propagation rate constant, and k_t is the termination rate constant). k_pk increased with an increasing molar fraction of ethyl acrylate in the initial feed. The monomer mixture and copolymer compositions versus the overall monomer conversion were calculated with the data of ¹H‐NMR spectra. The incorporation of the styrene monomer into the copolymer structure was more favored than that of the ethyl acrylate monomer. Reducing the molar fraction of styrene in the initial feed intensified this. Drawing the molar fraction of styrene (or ethyl acrylate) in the copolymer chains versus that in the initial feed showed a tendency of the system toward random copolymerization. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci, 2007     

Synthesis and characterization of diethyl-dithiocarbamic acid 2-[4-(2-diethylthiocarbamoylsulfanyl-2-phenyl-acetyl)-2,5-dioxo-piperazin-1-yl]-2-oxo-1-phenyl-ethyl ester as new reversible addition-fragmentation chain transfer agent for polymerization of ethyl methacrylate

Diethyl-dithiocarbamic acid 2-[4-(2-diethylthiocarbamoylsulfanyl-2-phenyl-acetyl)-2,5-dioxo-piperazin-1-yl]-2-oxo-1-phenyl-ethyl ester as a novel di-functional reversible addition–fragmentation chain transfer (RAFT) agent was synthesized based on 2,5-diketopiperazine. The RAFT agent was designed based on the propagating core (R group) approach and characterized by ¹H NMR, ¹³C NMR, FT-IR, elemental analysis, and melting point technique. Then, ethyl methacrylate was synthesized via free radical and RAFT polymerizations. To investigate the effect of the RAFT agent on the kinetic of polymerization, molecular weight, and polydispersity index (PDI) of polymers and also monomer conversion were monitored. Also, synthesized polymers were characterized by ¹H NMR, ¹³C NMR, FT-IR, and TGA. Characterization analyses of synthesized RAFT agent were consistent with the structure. NMR and FTIR analyses confirmed end group incorporation of RAFT agent into polymer structure. According to results, poly(ethyl methacrylate) with low PDI (1.14) was obtained. Kinetic study indicated well-controlled polymerization of ethyl methacrylate by synthesized RAFT agent. TGA results showed that RAFT agent could reduce termination reactions and so reduce head-to-head bonds and chain-end unsaturation by keeping the concentration of radicals low enough.